50 Steps to Master #AgenticAI in 2025-26
by @ingliguori #LLM #GenerativeAI #ArtificialIntelligence #MachineLearning
LLMS
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50 Steps to Master AgenticAI in 2025-26
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Sakana Fugu uses multi-agent orchestration of LLMs recursively
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How does it work? Sakana Fugu is itself an LLM, trained to call various LLMs in an agent pool, including instances of itself recursively. Fugu dynamically orchestrates the world's best models to tackle complex, multi-step tasks. As shown in this figure, Fugu is a multi-agent
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Fugu Matches Top Models; Orchestration Models Next Frontier
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Fugu stands shoulder-to-shoulder with leading models like Fable and Mythos across the industry's most rigorous engineering, scientific, and reasoning benchmarks. Read the full blog: https://
sakana.ai/fugu-release Beyond Bigger Models: Why are Orchestration Models the Next Frontier -
Question on DiffusionGemma’s transparency and hidden reasoning
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What is the transparency of DiffusionGemma? Given how diffusion language models (Diffusion LMs) denoise tokens instead of generating them left to right, there is a concern about how reasoning will be hidden in
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LLMs prove reasoning can emerge from language statistics
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I am not garymarcusing here. LLMs are proof that it is possible to distill intelligent reasoning behavior by doing statistics over human language patterns
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Using GLM 5.2 in Cursor with MagicPath setup guide
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I’ve been using GLM 5.2 in Cursor with MagicPath and really like it so far.
— Pietro Schirano (@skirano) 21 juin 2026
Cursor Settings → Models:
Add your Fireworks key under “OpenAI API Key” and enable it
Base URL: https://t.co/2Bgawq3FJM
Model: accounts/fireworks/models/glm-5p2
Restart Cursor. Done. pic.twitter.com/fHpmqljQXdI’ve been using GLM 5.2 in Cursor with MagicPath and really like it so far. Cursor Settings → Models:
Add your Fireworks key under “OpenAI API Key” and enable it
Base URL: https://
api.fireworks.ai/inference/v1
Model: accounts/fireworks/models/glm-5p2 Restart Cursor. Done. -
Ollama slower, slop, code thieves; better alternatives listed
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ollama > slower than llama.cpp on windows
> slower than mlx on mac
> slop useless wrapper
> literal code thieves alternatives? > lmstudio
> llama.cpp
> exllamav2/v3
> vllm
> sglang
> trt-llm literally anythingʼs better than ollama -
Why focus on inference engines: performance gains with vLLM and Sglang
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Why do I focus on Inference Engines/Software Stacks for your hardware? – 2x RTX 3090s: ~14.5 tok/s → ~64 tok/s moving to vLLM w/ TP=2 – RTX PRO 6000: ~32 tok/s → ~110 tok/s moving to Sglang So: – CUDA/2+ GPUs: ExLlamaV3/vLLM/Sglang > llama.cpp – Edge: llama.cpp > Ollama
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ChatGPT record and replay unavailable in Europe
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gdammit, chatgpt record and replay not avail in europa